Neural Networks Technique for the Control of Artificial Mobile Agents

Abstract

This project presents a neural network approach to the motion control of an artificial mobile robot. The robot is required to move towards its goal in a cluttered environment and simultaneously avoiding collision with obstacles of any size and being randomly distributed. We have used feed-forward back propagation algorithm to control the robot motion. Our main focus is on the steering action of the robot. The algorithm takes input as angle to target, front obstacle distance, right obstacle distance and left obstacle distance. Sensors have been used for the purpose of getting the inputs on various obstacle distances. The network provides the angle through which the robot is to be steered to avoid obstacles. The steering is achieved by controlling the speed of motors and hence of wheels to whom they are attached to. The viability of the algorithm has been demonstrated through various simulations. The real-world experimental outcomes prove the efficacy of the algorithm to navigate the robot successfully.